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Conduct applied AI research and development for construction take-off and estimation using computer vision, NLP, deep learning, and multimodal models. Build production-ready systems that extract insights from blueprints, scanned PDFs, SVGs, and other unstructured documents. Prototype, validate, optimize, and deploy models while collaborating with software engineering, research, and product teams. Apply data-centric practices, experiment tracking, reproducibility, and model optimization techniques.

Conduct applied computer vision and deep learning research for automated construction drawing takeoff and estimation. Own a scoped research problem, train and evaluate models for detection, segmentation, or structured extraction, explore self-supervised learning and multimodal signals, develop prototypes using real data, design evaluations, and present findings to the research team.

Own end-to-end computer vision and AI research for automated construction takeoff and estimation. Develop deep learning models for object detection, segmentation, structured extraction, and multimodal understanding of vector and raster drawings. Build evaluation frameworks, train models at scale, make data-labeling decisions, assess current research, and productionize successful models. The role requires strong Python and PyTorch skills, GPU and distributed training experience, and practical computer vision expertise.

Own end-to-end computer vision and deep learning research for automated construction drawing takeoff and estimation. Develop object detection, segmentation, structured extraction, and multimodal models using visual layout, geometry, and text. Design evaluation frameworks, determine effective data strategies, assess current research, and deploy models to production. The role requires strong Python and PyTorch expertise, GPU and distributed training experience, and practical computer vision research depth.